scholarly journals A bioinformatics approach for mass spectrometry data processing: Applications to proteomics and small molecule analysis

2002 ◽  
Vol 16 (2) ◽  
pp. 81-87
Author(s):  
Martin Sonderegger ◽  
Kristin Staniszewski ◽  
Andrew Meyers ◽  
Gary Siuzdak
Keyword(s):  
Mass Spectrometry ◽  
Data Processing ◽  
Portable Document Format ◽  
Mass Spectrometry Data ◽  
Web Browser ◽  
Mass Spectrometers ◽  
Web Based ◽  
Mass Spectral ◽  
Microsoft Access ◽  
To Receive

We have developed a web‒based software system,JULIAN, that simplifies the process of relaying mass spectral information for chemists, protein chemists, biochemists and all others performing mass spectrometry experiments through a centralized mass spectrometry laboratory.JULIANallows for relative ease in submitting compound information as well as instant access to analysis results from any networked computer equipped with a web browser. Compound information is centralized in a Microsoft Access database and results are available in Adobe's portable document format (PDF) from an NT4 server. This gives researchers the ability to easily obtain data and allows the analysts in the mass spectrometry lab to browse analysis results when assisting researchers with their inquiries. Due to this web‒based designJULIANis independent of the mass spectrometers' hardware and operating system. Approximately seven hundred on‒site and off‒site users have utilizedJULIANtransmitting over 40,000 analyses. The conversion from paper to electronic mass spectrometry data processing has enabled our Center to receive compound information, perform analysis, and relay the results four times faster than required previously.

scholarly journals GNPS Dashboard: Collaborative Analysis of Mass Spectrometry Data in the Web Browser

2021 ◽  
Author(s):  
Daniel Petras ◽  
Vanessa V Phelan ◽  
Deepa D Acharya ◽  
Andrew E Allen ◽  
Allegra T Aron ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Mass Spectrometry Data ◽  
Visualization Tool ◽  
Critical Aspect ◽  
Web Browser ◽  
Web Based ◽  
Private And Public ◽  
Collaborative Analysis ◽  
The Web

Access to web-based platforms has enabled scientists to perform research remotely. A critical aspect of mass spectrometry data analysis is the inspection, analysis, and visualization of the raw data to validate data quality and confirm statistical observations. We developed the GNPS Dashboard, a web-based data visualization tool, to facilitate synchronous collaborative inspection, visualization, and analysis of private and public mass spectrometry data remotely.

scholarly journals spectrum_utils: A Python package for mass spectrometry data processing and visualization

2019 ◽  
Author(s):  
Wout Bittremieux
Keyword(s):  
Mass Spectrometry ◽  
Data Processing ◽  
Mass Spectrometry Data ◽  
Web Pages ◽  
Mass Spectral ◽  
File Formats ◽  
Online Documentation ◽  
High Level ◽  
Python Package ◽  
Publication Quality

AbstractGiven the wide diversity in applications of biological mass spectrometry, custom data analyses are often needed to fully interpret the results of an experiment. Such bioinformatics scripts necessarily include similar basic functionality to read mass spectral data from standard file formats, process it, and visualize it. Rather than having to reimplement this functionality, to facilitate this task, spectrum_utils is a Python package for mass spectrometry data processing and visualization. Its high-level functionality enables developers to quickly prototype ideas for computational mass spectrometry projects in only a few lines of code. Notably, the data processing functionality is highly optimized for computational efficiency to be able to deal with the large volumes of data that are generated during mass spectrometry experiments. The visualization functionality makes it possible to easily produce publication-quality figures as well as interactive spectrum plots for inclusion on web pages. spectrum_utils is available for Python 3.6+, includes extensive online documentation and examples, and can be easily installed using conda. It is freely available as open source under the Apache 2.0 license at https://github.com/bittremieux/spectrum_utils.

Mass Spectrometry Data Processing

Metabolomics ◽  
2019 ◽  
pp. 73-99
Author(s):  
Steffen Neumann ◽  
Oscar Yanes ◽  
Roland Mumm ◽  
Pietro Franceschi
Keyword(s):  
Mass Spectrometry ◽  
Data Processing ◽  
Mass Spectrometry Data

scholarly journals In Situ Mass Spectrometry Diagnostics of Impaired Glucose Tolerance Using Label-Free Metabolomic Signature

Diagnostics ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1052
Author(s):  
Petr G. Lokhov ◽  
Oxana P. Trifonova ◽  
Dmitry L. Maslov ◽  
Elena E. Balashova
Keyword(s):  
Mass Spectrometry ◽  
Glucose Tolerance ◽  
Data Processing ◽  
Impaired Glucose Tolerance ◽  
Characteristic Curve ◽  
Complex Data ◽  
Label Free ◽  
Mass Peak ◽  
Mass Spectrometers

In metabolomics, mass spectrometry is used to detect a large number of low-molecular substances in a single analysis. Such a capacity could have direct application in disease diagnostics. However, it is challenging because of the analysis complexity, and the search for a way to simplify it while maintaining the diagnostic capability is an urgent task. It has been proposed to use the metabolomic signature without complex data processing (mass peak detection, alignment, normalization, and identification of substances, as well as any complex statistical analysis) to make the analysis more simple and rapid. Methods: A label-free approach was implemented in the metabolomic signature, which makes the measurement of the actual or conditional concentrations unnecessary, uses only mass peak relations, and minimizes mass spectra processing. The approach was tested on the diagnosis of impaired glucose tolerance (IGT). Results: The label-free metabolic signature demonstrated a diagnostic accuracy for IGT equal to 88% (specificity 85%, sensitivity 90%, and area under receiver operating characteristic curve (AUC) of 0.91), which is considered to be a good quality for diagnostics. Conclusions: It is possible to compile label-free signatures for diseases that allow for diagnosing the disease in situ, i.e., right at the mass spectrometer without complex data processing. This achievement makes all mass spectrometers potentially versatile diagnostic devices and accelerates the introduction of metabolomics into medicine.

scholarly journals MzJava: An open source library for mass spectrometry data processing

2015 ◽  
Vol 129 ◽  
pp. 63-70 ◽  
Author(s):  
Oliver Horlacher ◽  
Frederic Nikitin ◽  
Davide Alocci ◽  
Julien Mariethoz ◽  
Markus Müller ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Processing ◽  
Open Source ◽  
Mass Spectrometry Data

scholarly journals Identification of Leishmania by Matrix-Assisted Laser Desorption Ionization–Time of Flight (MALDI-TOF) Mass Spectrometry Using a Free Web-Based Application and a Dedicated Mass-Spectral Library

2017 ◽  
Vol 55 (10) ◽  
pp. 2924-2933 ◽  
Author(s):  
Laurence Lachaud ◽  
Anna Fernández-Arévalo ◽  
Anne-Cécile Normand ◽  
Patrick Lami ◽  
Cécile Nabet ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Leishmania Donovani ◽  
Identification System ◽  
Enzyme Electrophoresis ◽  
Mass Spectral Library ◽  
Spectral Library ◽  
Web Based ◽  
Content Type ◽  
Mass Spectral ◽  
Scalable Database

ABSTRACT Human leishmaniases are widespread diseases with different clinical forms caused by about 20 species within the Leishmania genus. Leishmania species identification is relevant for therapeutic management and prognosis, especially for cutaneous and mucocutaneous forms. Several methods are available to identify Leishmania species from culture, but they have not been standardized for the majority of the currently described species, with the exception of multilocus enzyme electrophoresis. Moreover, these techniques are expensive, time-consuming, and not available in all laboratories. Within the last decade, mass spectrometry (MS) has been adapted for the identification of microorganisms, including Leishmania . However, no commercial reference mass-spectral database is available. In this study, a reference mass-spectral library (MSL) for Leishmania isolates, accessible through a free Web-based application (mass-spectral identification [MSI]), was constructed and tested. It includes mass-spectral data for 33 different Leishmania species, including species that infect humans, animals, and phlebotomine vectors. Four laboratories on two continents evaluated the performance of MSI using 268 samples, 231 of which were Leishmania strains. All Leishmania strains, but one, were correctly identified at least to the complex level. A risk of species misidentification within the Leishmania donovani , L. guyanensis , and L. braziliensis complexes was observed, as previously reported for other techniques. The tested application was reliable, with identification results being comparable to those obtained with reference methods but with a more favorable cost-efficiency ratio. This free online identification system relies on a scalable database and can be implemented directly in users' computers.

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